EDA

Import Packages

# Import Packages
pacman::p_load(sf, tmap, tidyverse, sfdep, readxl, Kendall, plotly, plyr, SpatialAcc, ggstatsplot, reshape2, fca, sp, caret, stplanr, broom, geojsonio, jsonlite, dplyr)

package 'rlang' successfully unpacked and MD5 sums checked
package 'tibble' successfully unpacked and MD5 sums checked
package 'vctrs' successfully unpacked and MD5 sums checked
package 'dplyr' successfully unpacked and MD5 sums checked
package 'ggstatsplot' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\la935\AppData\Local\Temp\RtmpAFw46O\downloaded_packages

Import Data

MPSZ

# Import MPSZ
mpsz <- st_read(dsn = "data/geospatial", layer = "MPSZ-2019")

Reading layer `MPSZ-2019' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84

mpsz <- st_transform(mpsz, 3414)

is.null(mpsz)

[1] FALSE

mpsz <- st_make_valid(mpsz)

write_rds(mpsz, "data/model/mpsz.rds")

MRT

# Import MRT Stations
mrt <- st_read(dsn = "data/geospatial", layer = "RapidTransitSystemStation")

Reading layer `RapidTransitSystemStation' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 220 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 6068.209 ymin: 27478.44 xmax: 45377.5 ymax: 47913.58
Projected CRS: SVY21

mrt_sf <- st_transform(mrt, 3414)

mrt_sf <- mrt_sf %>%
  filter(str_detect(mrt_sf$STN_NAM_DE, "MRT STATION|LRT STATION")) %>%
  dplyr::select(c(4, 3, 5))

mrt_sf[rowSums(is.na(mrt_sf))!=0,]

Simple feature collection with 0 features and 2 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] STN_NAM_DE TYP_CD_DES geometry  
<0 rows> (or 0-length row.names)

st_is_valid(mrt_sf)

  [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [37]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [49]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [61]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [73]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [85]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [97]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[109]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[121]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[133]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[145]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[157]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[169]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[181]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[193]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
[205]  TRUE

mrt_sf <- st_make_valid(mrt_sf)

write_rds(mrt_sf, "data/model/mrt_sf.rds")

MRT Rail Line

# MRT Rail Line
rail <- st_read(dsn = "data/geospatial", layer = "G_MP14_RAIL_LI")

Reading layer `G_MP14_RAIL_LI' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 1399 features and 9 fields (with 2 geometries empty)
Geometry type: MULTILINESTRING
Dimension:     XY
Bounding box:  xmin: 5950.856 ymin: 26030.91 xmax: 45473.16 ymax: 48252.23
Projected CRS: SVY21

rail_sf <- st_transform(rail, 3414)

rail_sf <- rail_sf %>%
  filter(TYPE %in%
           c("MRT",
             "LRT",
             "CCL")) %>%
  dplyr::select(c(1))

rail_sf[rowSums(is.na(rail_sf))!=0,]

Simple feature collection with 0 features and 1 field
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] OBJECTID geometry
<0 rows> (or 0-length row.names)

st_is_valid(rail_sf)

   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [99] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [113] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [127] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [141] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [155] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [183] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [197] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [225] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [239] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [267] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [281] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [295] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [309] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [323] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [337] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [351] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [365] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [379] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [393] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [407] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [435] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [449] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [463] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [477] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [491] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [505] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [519] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [533] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [547] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [561] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [575] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [589] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [603] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [617] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [631] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [645] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [659] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [673] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [687] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [701] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [715] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [729] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [743] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [771] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [785] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [799] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [813] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [827] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [855] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [869] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [883] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [897] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [911] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [925] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [939] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [953] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [967] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [981] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [995] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1009] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1023] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1037] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1051] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1065] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1079] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1093] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1107] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1135] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1149] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1163] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1177] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1191] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1219] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1233] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1247] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1261] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1275] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1303] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1317] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1345] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1359] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1387] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

rail_sf <- st_make_valid(rail_sf)

is.null(rail_sf)

[1] FALSE

rail_sf <- na.omit(rail_sf)

summary(rail_sf)

    OBJECTID                 geometry   
 Min.   :   1.0   MULTILINESTRING:1396  
 1st Qu.: 351.8   epsg:3414      :   0  
 Median : 700.5   +proj=tmer...  :   0  
 Mean   : 700.5                         
 3rd Qu.:1049.2                         
 Max.   :1399.0                         

write_rds(rail_sf, "data/model/rail_sf.rds")

Tourism

# Tourism
tourism_sf <- st_read(dsn = "data/geospatial", layer = "TOURISM")

Reading layer `TOURISM' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 107 features and 19 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -1.797693e+308 ymin: -1.797693e+308 xmax: 43659.54 ymax: 47596.73
Projected CRS: SVY21

# Assign EPSG Code
tourism_sf <- st_transform(tourism_sf, 3414) %>%
  dplyr::select(c(6, 10, 20))

tourism_sf[rowSums(is.na(tourism_sf))!=0,]

Simple feature collection with 2 features and 2 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -1.797693e+308 ymin: -1.797693e+308 xmax: 28447.06 ymax: 28853.86
Projected CRS: SVY21 / Singapore TM
                PAGETITLE ADDRESS                       geometry
4              Shophouses    <NA>      POINT (28447.06 28853.86)
73 Cruises from Singapore    <NA> POINT (-1.797693e+308 -1.79...

tourism_sf <- na.omit(tourism_sf,c("Shophouses", "Cruises from Singapore"))

is(tourism_sf, "sf")

[1] TRUE

write_rds(tourism_sf, "data/model/tourism_sf.rds")

Shopping Malls

# Shopping Malls
shopping <- read.csv("data/geospatial/mall_coordinates.csv")

shopping <- shopping %>%
  dplyr::select(name, latitude, longitude)

shopping_sf <- st_as_sf(shopping,
                              coords = c("longitude",
                                         "latitude"),
                              crs = 4326) %>%
  st_transform(crs = 3414)

write_rds(shopping_sf, "data/model/shopping_sf.rds")

Childcare

# Childcare
childcare_sf <- st_read(dsn = "data/geospatial", layer = "CHILDCARE")

Reading layer `CHILDCARE' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 1545 features and 15 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 11203.01 ymin: 25667.6 xmax: 45404.24 ymax: 49300.88
Projected CRS: WGS_1984_Transverse_Mercator

# Assign EPSG Code
childcare_sf <- st_transform(childcare_sf, 3414) %>%
  dplyr::select(c(10, 4))

childcare_sf[rowSums(is.na(childcare_sf))!=0,]

Simple feature collection with 0 features and 2 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME       ADDRESSSTR geometry  
<0 rows> (or 0-length row.names)

st_is_valid(childcare_sf)

   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
  [99] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [113] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [127] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [141] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [155] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [183] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [197] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [225] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [239] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [267] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [281] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [295] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [309] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [323] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [337] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [351] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [365] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [379] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [393] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [407] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [435] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [449] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [463] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [477] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [491] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [505] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [519] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [533] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [547] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [561] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [575] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [589] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [603] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [617] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [631] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [645] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [659] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [673] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [687] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [701] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [715] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [729] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [743] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [771] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [785] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [799] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [813] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [827] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [855] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [869] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [883] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [897] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [911] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [925] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [939] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [953] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [967] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [981] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [995] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1009] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1023] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1037] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1051] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1065] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1079] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1093] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1107] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1135] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1149] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1163] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1177] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1191] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1219] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1233] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1247] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1261] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1275] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1303] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1317] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1345] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1359] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1387] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1401] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1415] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1429] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1443] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1457] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1471] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1485] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1499] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1513] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1527] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1541] TRUE TRUE TRUE TRUE TRUE

rail_sf <- st_make_valid(childcare_sf)

is.null(childcare_sf)

[1] FALSE

childcare_sf <- na.omit(childcare_sf)

write_rds(childcare_sf, "data/new/childcare_sf.rds")

Eldercare

# Eldercare
eldercare_sf <- st_read(dsn = "data/geospatial", layer = "ELDERCARE")

Reading layer `ELDERCARE' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 133 features and 18 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 14481.92 ymin: 28218.43 xmax: 41665.14 ymax: 46804.9
Projected CRS: SVY21

# Assign EPSG Code
eldercare_sf <- st_transform(eldercare_sf, 3414) %>%
  dplyr::select(c(11, 5, 4))

eldercare_sf[rowSums(is.na(eldercare_sf))!=0,]

Simple feature collection with 0 features and 3 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME       ADDRESSSTR ADDRESSPOS geometry  
<0 rows> (or 0-length row.names)

rail_sf <- st_make_valid(eldercare_sf)

is.null(eldercare_sf)

[1] FALSE

eldercare_sf <- na.omit(eldercare_sf)

write_rds(eldercare_sf, "data/new/eldercare_sf.rds")

Hawker Centre

# Hawker Centre
hawker_sf <- st_read(dsn = "data/geospatial", layer = "HAWKERCENTRE")

Reading layer `HAWKERCENTRE' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 125 features and 21 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 12874.19 ymin: 28355.97 xmax: 45241.4 ymax: 47872.53
Projected CRS: SVY21

# Assign EPSG Code
hawker_sf <- st_transform(hawker_sf, 3414) %>%
  dplyr::select(c(13))

hawker_sf[rowSums(is.na(hawker_sf))!=0,]

Simple feature collection with 0 features and 1 field
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME     geometry
<0 rows> (or 0-length row.names)

rail_sf <- st_make_valid(hawker_sf)

is.null(hawker_sf)

[1] FALSE

hawker_sf <- na.omit(hawker_sf)

write_rds(hawker_sf, "data/new/hawker_sf.rds")

Kindergartens

# Kindergartens
kindergarten_sf <- st_read(dsn = "data/geospatial", layer = "KINDERGARTENS")

Reading layer `KINDERGARTENS' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 448 features and 15 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 11909.7 ymin: 25596.33 xmax: 43395.47 ymax: 48562.06
Projected CRS: SVY21

# Assign EPSG Code
kindergarten_sf <- st_transform(kindergarten_sf, 3414) %>%
  dplyr::select(c(11, 5))

kindergarten_sf[rowSums(is.na(kindergarten_sf))!=0,]

Simple feature collection with 0 features and 2 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME       ADDRESSSTR geometry  
<0 rows> (or 0-length row.names)

rail_sf <- st_make_valid(kindergarten_sf)

is.null(kindergarten_sf)

[1] FALSE

kindergarten_sf <- na.omit(kindergarten_sf)

write_rds(kindergarten_sf, "data/new/kindergarten_sf.rds")

National Park

# National Park
national_sf <- st_read(dsn = "data/geospatial", layer = "NATIONALPARKS")

Reading layer `NATIONALPARKS' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 352 features and 15 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 12373.11 ymin: 21869.93 xmax: 46735.95 ymax: 49231.09
Projected CRS: SVY21

# Assign EPSG Code
national_sf <- st_transform(national_sf, 3414) %>%
  dplyr::select(c(7))

national_sf[rowSums(is.na(national_sf))!=0,]

Simple feature collection with 0 features and 1 field
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] NAME     geometry
<0 rows> (or 0-length row.names)

national_sf <- st_make_valid(national_sf)

is.null(national_sf)

[1] FALSE

national_sf <- na.omit(national_sf)

write_rds(national_sf, "data/new/national_sf.rds")

Supermarkets

# Supermarkets
supermarkets_sf <- st_read(dsn = "data/geospatial", layer = "SUPERMARKETS")

Reading layer `SUPERMARKETS' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 526 features and 8 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 4901.188 ymin: 25529.08 xmax: 46948.22 ymax: 49233.6
Projected CRS: SVY21

# Assign EPSG Code
supermarkets_sf <- st_transform(supermarkets_sf, 3414) %>%
  dplyr::select(c(1, 5, 6))

supermarkets_sf[rowSums(is.na(supermarkets_sf))!=0,]

Simple feature collection with 0 features and 3 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] LIC_NAME POSTCODE LIC_NO   geometry
<0 rows> (or 0-length row.names)

supermarkets_sf <- st_make_valid(supermarkets_sf)

is.null(supermarkets_sf)

[1] FALSE

supermarkets_sf <- na.omit(supermarkets_sf)

write_rds(supermarkets_sf, "data/new/supermarkets_sf.rds")

Hexagon

# Hexagon for Map
hexagon <- st_read(dsn = "data/geospatial", layer = "hexagons")

Reading layer `hexagons' from data source 
  `C:\wamp64\www\IS415-Project\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 3125 features and 6 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 21506.33 xmax: 50010.26 ymax: 50256.33
Projected CRS: SVY21 / Singapore TM

hexagon_sf <- st_transform(hexagon, 3414)

write_rds(hexagon_sf, "data/model/hexagon_sf.rds")

Population

# Population Data 2022
popdata <- read_csv("data/aspatial/respopagesexfa2022.csv")

popdata2022 <- popdata %>%
  spread(AG, Pop) %>%
  mutate(YOUNG = `0_to_4`+`5_to_9`+`10_to_14`+
`15_to_19`+`20_to_24`) %>%
mutate(`ECONOMY ACTIVE` = rowSums(.[9:13])+
rowSums(.[15:17]))%>%
mutate(`AGED`=rowSums(.[18:22])) %>%
mutate(`TOTAL`=rowSums(.[5:22])) %>%  
mutate(`DEPENDENCY` = (`YOUNG` + `AGED`)
/`ECONOMY ACTIVE`) %>%
mutate_at(.vars = vars(PA, SZ), toupper) %>%
dplyr::select(`PA`, `SZ`, `YOUNG`, `ECONOMY ACTIVE`, `AGED`, 
       `TOTAL`, `DEPENDENCY`) %>%
  filter(`ECONOMY ACTIVE` > 0)

popdata2022 <- popdata2022 %>%
  mutate_at(.vars = vars(PA, SZ), 
          .funs = funs(toupper)) %>%
  filter(`ECONOMY ACTIVE` > 0)

write_rds(popdata2022, "data/model/popdata2022.rds")

mpsz_popdata2022 <- left_join(mpsz, popdata2022, by = c("SUBZONE_N" = "SZ"))

write_rds(mpsz_popdata2022, "data/model/mpsz_popdata2022.rds")

summary(mpsz_popdata2022$DEPENDENCY)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.6786  0.8645  0.9583  1.0000 17.0000      99 

EDA - Visualisation

MRT & MPSZ

mrt_mpsz <- st_join(mrt_sf, mpsz)

mrt_mpsz <- mrt_mpsz %>%
  select(c(1,7))

mrt_mpsz <- na.omit(mrt_mpsz)

# MRT vs MPSZ
tmap_mode('view')
tmap_options(check.and.fix = TRUE) +
tm_shape(mpsz) +
  tm_polygons() +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07) +
  tm_view(set.zoom.limits = c(11,14))

write_rds(mrt_mpsz, "data/model/mrt_mpsz.rds")

MRT & Population Density

# MRT vs Population Density
tmap_mode('view')
tm_shape(mpsz_popdata2022) +
  tm_fill("DEPENDENCY",
          breaks = c(0, 0.60, 0.70, 0.80, 0.90, 1.00)) +
  tm_borders(lwd = 0.1,  alpha = 1) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14))

MRT & Tourist Attraction

# MRT vs Tourism
tmap_mode('view')
tmap_options(check.and.fix = TRUE)
tm_shape(tourism_sf) +
  tm_dots(size = 0.1, col = 'yellow') +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14))

MRT & Shopping Malls

# MRT vs Shopping Malls
tmap_mode('view')
tmap_options(check.and.fix = TRUE)
tm_shape(shopping_sf) +
  tm_dots(size = 0.1, col = 'yellow') +
tm_shape(mrt_sf) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14))

Accessibility

Import Shapefiles

# Hexagon for Map
hexagon <- readRDS("data/model/hexagon_sf.rds")

# Tourism
tourism <- readRDS("data/model/tourism_sf.rds")

shopping <- readRDS("data/model/shopping_sf.rds")

# Import MRT Stations
mrt_mpsz <- readRDS("data/model/mrt_mpsz.rds")

#Import MRT Rail
mrt_rail <- readRDS("data/model/rail_sf.rds")

Calculate Distance Matrix

hexagon <- hexagon %>%
  dplyr::select(fid) %>%
  mutate(demand = 100)

mrt_mpsz <- mrt_mpsz %>%
  mutate(demand = 100)

shopping <- shopping %>%
  mutate(capacity = 100)

tourism <- tourism %>%
  dplyr::select(PAGETITLE) %>%
  mutate(capacity = 100)

Hexagon x Tourism

tourism_coord <- st_coordinates(tourism)
hexagon_coord <- st_centroid(hexagon) %>%
  st_coordinates()

hex_tour <- SpatialAcc::distance(hexagon_coord,
                                  tourism_coord,
                                  type = "euclidean")

hex_tour_km <- as.matrix(hex_tour/1000)

Hansen

acc_Hansen <- data.frame(ac(hexagon$demand,
                            tourism$capacity,
                            hex_tour_km,
                            #d0 = 50,
                            power = 2,
                            family = "Hansen"))

colnames(acc_Hansen) <- "accHansen"
acc_Hansen <- as_tibble(acc_Hansen)
hex_tour_Hansen <- bind_cols(hexagon, acc_Hansen)

write_rds(hex_tour_Hansen, "data/model/hex_tour_Hansen.rds")

tmap_mode("view")
tm_shape(hex_tour_Hansen,
         bbox = st_bbox(hex_tour_Hansen)) + 
  tm_fill(col = "accHansen",
          n = 10,
          style = "quantile",
          border.col = "black",
          border.lwd = 1) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14)) +
tm_shape(tourism) +
  tm_symbols(size = 0.1) +
  tm_layout(main.title = "Accessibility to Tourism: Hansen Method",
            main.title.position = "center",
            main.title.size = 2,
            legend.outside = FALSE,
            legend.height = 0.45, 
            legend.width = 3.0,
            legend.format = list(digits = 6),
            legend.position = c("right", "top"),
            frame = TRUE) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar(width = 0.15) +
  tm_grid(lwd = 0.1, alpha = 0.5)

KD2SFCA

acc_KD2SFCA <- data.frame(ac(hexagon$demand,
                            tourism$capacity,
                            hex_tour_km,
                            d0 = 50,
                            power = 2,
                            family = "KD2SFCA"))

colnames(acc_KD2SFCA) <- "accKD2SFCA"
acc_KD2SFCA <- as_tibble(acc_KD2SFCA)
hex_tour_KD2SFCA <- bind_cols(hexagon, acc_KD2SFCA)

write_rds(hex_tour_KD2SFCA, "data/model/hex_tour_KD2SFCA.rds")

tmap_mode("view")
tm_shape(hex_tour_KD2SFCA,
         bbox = st_bbox(hex_tour_KD2SFCA)) + 
  tm_fill(col = "accKD2SFCA",
          n = 10,
          style = "quantile",
          border.col = "black",
          border.lwd = 1) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14)) +
tm_shape(tourism) +
  tm_symbols(size = 0.1) +
  tm_layout(main.title = "Accessibility to Tourism: Hansen Method",
            main.title.position = "center",
            main.title.size = 2,
            legend.outside = FALSE,
            legend.height = 0.45, 
            legend.width = 3.0,
            legend.format = list(digits = 6),
            legend.position = c("right", "top"),
            frame = TRUE) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar(width = 0.15) +
  tm_grid(lwd = 0.1, alpha = 0.5)

SAM

acc_SAM <- data.frame(ac(hexagon$demand,
                            tourism$capacity,
                            hex_tour_km,
                            d0 = 50,
                            power = 2,
                            family = "SAM"))

colnames(acc_SAM) <- "accSAM"
acc_SAM <- as_tibble(acc_SAM)
hex_tour_SAM <- bind_cols(hexagon, acc_SAM)

write_rds(hex_tour_SAM, "data/model/hex_tour_SAM.rds")

tmap_mode("view")
tm_shape(hex_tour_SAM,
         bbox = st_bbox(hex_tour_SAM)) + 
  tm_fill(col = "accSAM",
          n = 10,
          style = "quantile",
          border.col = "black",
          border.lwd = 1) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.5, size=0.07)+
  tm_view(set.zoom.limits = c(11,14)) +
tm_shape(tourism) +
  tm_symbols(size = 0.1) +
  tm_layout(main.title = "Accessibility to Tourism: Hansen Method",
            main.title.position = "center",
            main.title.size = 2,
            legend.outside = FALSE,
            legend.height = 0.45, 
            legend.width = 3.0,
            legend.format = list(digits = 6),
            legend.position = c("right", "top"),
            frame = TRUE) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar(width = 0.15) +
  tm_grid(lwd = 0.1, alpha = 0.5)

MRT x Tourism

tourism_coord <- st_coordinates(tourism)
mrt_coord <- st_centroid(mrt_mpsz) %>%
  st_coordinates()

mrt_tour <- SpatialAcc::distance(mrt_coord,
                                  tourism_coord,
                                  type = "euclidean")

mrt_tour_km <- as.matrix(mrt_tour/1000)

Hansen

acc_Hansen <- data.frame(ac(mrt_mpsz$demand,
                            tourism$capacity,
                            mrt_tour_km,
                            #d0 = 50,
                            power = 2,
                            family = "Hansen"))

colnames(acc_Hansen) <- "accHansen"
acc_Hansen <- as_tibble(acc_Hansen)
mrt_tour_Hansen <- bind_cols(mrt_mpsz, acc_Hansen)

write_rds(mrt_tour_Hansen, "data/model/mrt_tour_Hansen.rds")

# MRT & Tourism
tmap_mode('view')
tm_shape(tourism) +
  tm_symbols(size = 0.5) +
tm_shape(mrt_mpsz) +
  tm_dots(alph=0.1, size=0.1)+
  tm_view(set.zoom.limits = c(11,14)) +
tm_shape(mrt_tour_Hansen) +
  tm_bubbles(col = "accHansen",
             n = 5,
             style = "quantile",
             size = 0.2,
             border.col = "black",
             border.lwd = 1)

Boxplot

mrt_tour_boxplot <- mrt_tour_Hansen

write_rds(mrt_tour_boxplot, "data/model/mrt_tour_boxplot.rds")

ggplot(data=mrt_tour_boxplot, 
       aes(y = log(accHansen), 
           x= REGION_N)) +
  geom_boxplot() +
  stat_summary(fun.y = "mean", 
                   geom = "point", 
                   colour = "red", 
                   size = 2)